Community biomass and life history benchmarks for coral reef fisheries

Abstract

The status of fisheries requires establishing and evaluating benchmarks derived from unfished ecosystems. Habitats, environmental conditions, properties of the fish communities and management systems could potentially influence the variability surrounding benchmarks. Consequently, eighteen variables including habitat, number of species, life histories, thermal and productivity environments were tested for influences on reef fish biomass in 62 reefs within old high compliance closures along the east African coastline. Biomass and weighted life history characteristics were classified and described for total, fishable, target and non‐target groups. Benchmark biomass fell within a 95% confidence interval of ~1,030–1,250 kg/ha and equally distributed among target and non‐target groups. While some relationships were statistically significant, most were weak, poorly sampled (ocean exposed reefs), had uncertain relationships with biomass (number of species), or the explained variation was bounded within the above confidence intervals (habitat and environment). Therefore, a regional unfished biomass benchmark (B0) of 1,150 and 560 kg/ha is recommended for total and target biomasses, respectively. Weighted life history metrics indicate that the target had slower life histories than the non‐target fish communities. Consequently, they will be fished unsustainably if yield recommendations are derived from the total, resilient or non‐target fish life history metrics. The intrinsic rates of increase (r) and target categorization of biomass were the most influential metrics in estimating yields.

Multicriteria estimate of coral reef fishery sustainability

“…The Kenyan yield time‐series data and decay rates were a helpful source of additional information but still indicate considerable yield variability driven largely by initial effort and yields. Finally, knowing B 0 and r provided key empirical information for calibrating the MSY model but high variability in r indicates that the recovery rate is the key source of the yield uncertainty (McClanahan, ).…”

“…Consequently, models indicate that the MMSY yields should occur where biomass is around half of this pristine biomass level. However, biomass alone does not predict yields, which are highly sensitive to biomass recovery rates (McClanahan, ). Consequently, knowing and maintaining high recovery rates will be critical to any fisheries policies aimed at promoting sustainable yields and maintaining high fisheries production.…”

“…From the same data source, reported recovery rates were r = 0.23 ± 0.16 (95% CI), which produces mean recovery times between ~18 and 23 years. z values are unknown for coral reef fishes but well‐studied temperate fisheries stocks indicate lower and upper values of 0.8 and 1.2 (Thorson, Cope, Branch, & Jensen, ), which has minor effects on reef fish yields (McClanahan, ). Additionally, I also present a fisheries management working rule that multispecies MSY (MMSY) can be achieved when biomass is between 0.25 and 0.5 B 0 (Hilborn & Stokes, ).…”

“…Consequently, using high TA B L E 1 Ecosystem-based management fisheries goals, metrics, indicators of unsustainability and proposed management decisions and actions. Recommendations based on studies of coral reef fisheries in the western Indian Ocean (McClanahan, 2018;McClanahan et al, 2011) The second downward deflection point method also lacks many supporting empirical cases from tropical fisheries (McClanahan, Hicks, & Darling, 2008). Where profits, rather than food security or employment, drive fishing effort lost net profits often produces an inflexion point and a close relationship between MSY and maximum profits (Froese, Zeller, Kleisner, & Pauly, 2012).…”

“…While this B 0 metric helps set a window to evaluate status, the potential range of yields still ranges over ~12 tonnes km −2 year −1 due to variation in r estimates (yellow box,Figure 1). Clearly, recovery rates are the key variable for evaluating actual rather than relative yields(McClanahan, 2018).…”